# Adaptive Mechanism Design: Learning to Promote Cooperation

arXiv: https://arxiv.org/abs/1806.04067 · HTML: https://mlsanity.com/paper/1806.04067/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jun 2018.

## Built on by (most cited first, 9 of 9)

- [Principal-Agent Reinforcement Learning: Orchestrating AI Agents with Contracts](https://mlsanity.com/paper/2407.18074/) (2024)
- [Modelling Cooperation in Network Games with Spatio-Temporal Complexity](https://mlsanity.com/paper/2102.06911/) (2021)
- [Mediated Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2306.08419/) (2023)
- [Deception in Social Learning: A Multi-Agent Reinforcement Learning Perspective](https://mlsanity.com/paper/2106.05402/) (2021)
- [IQ-Flow: Mechanism Design for Inducing Cooperative Behavior to Self-Interested Agents in Sequential Social Dilemmas](https://mlsanity.com/paper/2302.14604/) (2023)
- [Learning to Steer Markovian Agents under Model Uncertainty](https://mlsanity.com/paper/2407.10207/) (2024)
- [Deep Incentive Design with Differentiable Equilibrium Blocks](https://mlsanity.com/paper/2603.07705/) (2026)
- [Steering No-Regret Agents in MFGs under Model Uncertainty](https://mlsanity.com/paper/2503.09309/) (2025)
- [Fair Contracts in Principal-Agent Games with Heterogeneous Types](https://mlsanity.com/paper/2506.15887/) (2025)

## Abstract (arXiv)

In the future, artificial learning agents are likely to become increasingly widespread in our society. They will interact with both other learning agents and humans in a variety of complex settings including social dilemmas. We consider the problem of how an external agent can promote cooperation between artificial learners by distributing additional rewards and punishments based on observing the learners' actions. We propose a rule for automatically learning how to create right incentives by considering the players' anticipated parameter updates. Using this learning rule leads to cooperation with high social welfare in matrix games in which the agents would otherwise learn to defect with high probability. We show that the resulting cooperative outcome is stable in certain games even if the planning agent is turned off after a given number of episodes, while other games require ongoing intervention to maintain mutual cooperation. However, even in the latter case, the amount of necessary additional incentives decreases over time.